Ben-David et al. [1]} have proven that the adaptation loss is bounded by three terms, e.g., the expected loss on source domain, the domain divergence, and the shared error of the ideal joint hypothesis on the source and target domain. Because the first term corresponds to the well-studied supervised learning problems and the third term is considered sufficiently low, the majority of recent works lay emphasis on the second term. In this spirit, some approaches focus on the distribution shift in the latent feature space [2]}, [3]}, [4]}, [5]}, [6]}, [7]}. Nevertheless, most of such methods only achieve in classification task while failing in segmentation. With a few exceptions, Hoffman et al. [3]} employed adversarial network to align the feature representations between domains and additionally appended category statistic constraints to the adversarial model. Apart from the feature-space DA, some methods address the problem in the pixel space [9]}, [10]}, which relates to the style transfer approaches [11]}, [12]} to make images indistinguishable across domains. Joint consideration of pixel- and feature-space domain adaptation is studied in [13]}. For segmentation task, it is also found that aligning the segmentation space is a more effective DA strategy [14]}, [15]}. Besides the adversarial training-based DA methods [13]}, [14]}, [9]}, other lines of work on semantic segmentation borrow the idea from self-training [19]} or co-training [20]}. The self-training-based DA [21]}, [22]} attempts to assign pseudo labels to target images and then use these labels to train the target model directly. While the co-training-based DA [23]}, [24]} aims to detect the domain-invariant features by maximizing the consensus of the multiple classifiers.
